Módulo 3: Apresentando suas análises
PROFESP, DEMSP, MS
Geographic Information System (GIS)
Vetor: Pontos, linhas e polígonos
Raster: Pixels
Geralmente em Shapefiles
coleção de arquivos: .shp, .shx, and .dbf. ou mais…
Localização na superfície da terra
Sistema de coordenadas
Pacotes, baixam direto os shapefiles
Ler de shapefiles no disco (seu computurador)
Não é um dataframe
Mas não importa, vamos fingir que é
Simple feature collection with 242 features and 168 fields
Geometry type: MULTIPOLYGON
Dimension: XY
Bounding box: xmin: -180 ymin: -89.99893 xmax: 180 ymax: 83.59961
Geodetic CRS: WGS 84
First 10 features:
featurecla scalerank labelrank sovereignt sov_a3
1 Admin-0 country 1 3 Zimbabwe ZWE
2 Admin-0 country 1 3 Zambia ZMB
3 Admin-0 country 1 3 Yemen YEM
4 Admin-0 country 3 2 Vietnam VNM
5 Admin-0 country 5 3 Venezuela VEN
6 Admin-0 country 6 6 Vatican VAT
7 Admin-0 country 1 4 Vanuatu VUT
8 Admin-0 country 1 3 Uzbekistan UZB
9 Admin-0 country 1 4 Uruguay URY
10 Admin-0 country 3 6 Federated States of Micronesia FSM
adm0_dif level type tlc admin adm0_a3
1 0 2 Sovereign country 1 Zimbabwe ZWE
2 0 2 Sovereign country 1 Zambia ZMB
3 0 2 Sovereign country 1 Yemen YEM
4 0 2 Sovereign country 1 Vietnam VNM
5 0 2 Sovereign country 1 Venezuela VEN
6 0 2 Sovereign country 1 Vatican VAT
7 0 2 Sovereign country 1 Vanuatu VUT
8 0 2 Sovereign country 1 Uzbekistan UZB
9 0 2 Sovereign country 1 Uruguay URY
10 0 2 Sovereign country 1 Federated States of Micronesia FSM
geou_dif geounit gu_a3 su_dif
1 0 Zimbabwe ZWE 0
2 0 Zambia ZMB 0
3 0 Yemen YEM 0
4 0 Vietnam VNM 0
5 0 Venezuela VEN 0
6 0 Vatican VAT 0
7 0 Vanuatu VUT 0
8 0 Uzbekistan UZB 0
9 0 Uruguay URY 0
10 0 Federated States of Micronesia FSM 0
subunit su_a3 brk_diff name
1 Zimbabwe ZWE 0 Zimbabwe
2 Zambia ZMB 0 Zambia
3 Yemen YEM 0 Yemen
4 Vietnam VNM 0 Vietnam
5 Venezuela VEN 0 Venezuela
6 Vatican VAT 0 Vatican
7 Vanuatu VUT 0 Vanuatu
8 Uzbekistan UZB 0 Uzbekistan
9 Uruguay URY 0 Uruguay
10 Federated States of Micronesia FSM 0 Micronesia
name_long brk_a3 brk_name brk_group abbrev postal
1 Zimbabwe ZWE Zimbabwe <NA> Zimb. ZW
2 Zambia ZMB Zambia <NA> Zambia ZM
3 Yemen YEM Yemen <NA> Yem. YE
4 Vietnam VNM Vietnam <NA> Viet. VN
5 Venezuela VEN Venezuela <NA> Ven. VE
6 Vatican VAT Vatican <NA> Vat. V
7 Vanuatu VUT Vanuatu <NA> Van. VU
8 Uzbekistan UZB Uzbekistan <NA> Uzb. UZ
9 Uruguay URY Uruguay <NA> Ury. UY
10 Federated States of Micronesia FSM Micronesia <NA> F.S.M. FSM
formal_en formal_fr
1 Republic of Zimbabwe <NA>
2 Republic of Zambia <NA>
3 Republic of Yemen <NA>
4 Socialist Republic of Vietnam <NA>
5 Bolivarian Republic of Venezuela República Bolivariana de Venezuela
6 State of the Vatican City <NA>
7 Republic of Vanuatu <NA>
8 Republic of Uzbekistan <NA>
9 Oriental Republic of Uruguay <NA>
10 Federated States of Micronesia <NA>
name_ciawf note_adm0 note_brk
1 Zimbabwe <NA> <NA>
2 Zambia <NA> <NA>
3 Yemen <NA> <NA>
4 Vietnam <NA> <NA>
5 Venezuela <NA> <NA>
6 Holy See (Vatican City) <NA> <NA>
7 Vanuatu <NA> <NA>
8 Uzbekistan <NA> <NA>
9 Uruguay <NA> <NA>
10 Micronesia, Federated States of <NA> <NA>
name_sort name_alt mapcolor7 mapcolor8 mapcolor9
1 Zimbabwe <NA> 1 5 3
2 Zambia <NA> 5 8 5
3 Yemen, Rep. <NA> 5 3 3
4 Vietnam <NA> 5 6 5
5 Venezuela, RB <NA> 1 3 1
6 Vatican (Holy See) Holy See 1 3 4
7 Vanuatu <NA> 6 3 7
8 Uzbekistan <NA> 2 3 5
9 Uruguay <NA> 1 2 2
10 Micronesia, Federated States of <NA> 5 2 4
mapcolor13 pop_est pop_rank pop_year gdp_md gdp_year
1 9 14645468 14 2019 21440 2019
2 13 17861030 14 2019 23309 2019
3 11 29161922 15 2019 22581 2019
4 4 96462106 16 2019 261921 2019
5 4 28515829 15 2019 482359 2014
6 2 825 2 2019 -99 2019
7 3 299882 10 2019 934 2019
8 4 33580650 15 2019 57921 2019
9 10 3461734 12 2019 56045 2019
10 13 113815 9 2019 401 2018
economy income_grp fips_10 iso_a2 iso_a2_eh
1 5. Emerging region: G20 5. Low income ZI ZW ZW
2 7. Least developed region 4. Lower middle income ZA ZM ZM
3 7. Least developed region 4. Lower middle income YM YE YE
4 5. Emerging region: G20 4. Lower middle income VM VN VN
5 5. Emerging region: G20 3. Upper middle income VE VE VE
6 2. Developed region: nonG7 2. High income: nonOECD VT VA VA
7 7. Least developed region 4. Lower middle income NH VU VU
8 6. Developing region 4. Lower middle income UZ UZ UZ
9 5. Emerging region: G20 3. Upper middle income UY UY UY
10 6. Developing region 4. Lower middle income FM FM FM
iso_a3 iso_a3_eh iso_n3 iso_n3_eh un_a3 wb_a2 wb_a3 woe_id woe_id_eh
1 ZWE ZWE 716 716 716 ZW ZWE 23425004 23425004
2 ZMB ZMB 894 894 894 ZM ZMB 23425003 23425003
3 YEM YEM 887 887 887 RY YEM 23425002 23425002
4 VNM VNM 704 704 704 VN VNM 23424984 23424984
5 VEN VEN 862 862 862 VE VEN 23424982 23424982
6 VAT VAT 336 336 336 -99 -99 23424986 23424986
7 VUT VUT 548 548 548 VU VUT 23424907 23424907
8 UZB UZB 860 860 860 UZ UZB 23424980 23424980
9 URY URY 858 858 858 UY URY 23424979 23424979
10 FSM FSM 583 583 583 FM FSM 23424815 23424815
woe_note adm0_iso adm0_diff adm0_tlc adm0_a3_us adm0_a3_fr
1 Exact WOE match as country ZWE <NA> ZWE ZWE ZWE
2 Exact WOE match as country ZMB <NA> ZMB ZMB ZMB
3 Exact WOE match as country YEM <NA> YEM YEM YEM
4 Exact WOE match as country VNM <NA> VNM VNM VNM
5 Exact WOE match as country VEN <NA> VEN VEN VEN
6 Exact WOE match as country VAT <NA> VAT VAT VAT
7 Exact WOE match as country VUT <NA> VUT VUT VUT
8 Exact WOE match as country UZB <NA> UZB UZB UZB
9 Exact WOE match as country URY <NA> URY URY URY
10 Exact WOE match as country FSM <NA> FSM FSM FSM
adm0_a3_ru adm0_a3_es adm0_a3_cn adm0_a3_tw adm0_a3_in adm0_a3_np adm0_a3_pk
1 ZWE ZWE ZWE ZWE ZWE ZWE ZWE
2 ZMB ZMB ZMB ZMB ZMB ZMB ZMB
3 YEM YEM YEM YEM YEM YEM YEM
4 VNM VNM VNM VNM VNM VNM VNM
5 VEN VEN VEN VEN VEN VEN VEN
6 VAT VAT VAT VAT VAT VAT VAT
7 VUT VUT VUT VUT VUT VUT VUT
8 UZB UZB UZB UZB UZB UZB UZB
9 URY URY URY URY URY URY URY
10 FSM FSM FSM FSM FSM FSM FSM
adm0_a3_de adm0_a3_gb adm0_a3_br adm0_a3_il adm0_a3_ps adm0_a3_sa adm0_a3_eg
1 ZWE ZWE ZWE ZWE ZWE ZWE ZWE
2 ZMB ZMB ZMB ZMB ZMB ZMB ZMB
3 YEM YEM YEM YEM YEM YEM YEM
4 VNM VNM VNM VNM VNM VNM VNM
5 VEN VEN VEN VEN VEN VEN VEN
6 VAT VAT VAT VAT VAT VAT VAT
7 VUT VUT VUT VUT VUT VUT VUT
8 UZB UZB UZB UZB UZB UZB UZB
9 URY URY URY URY URY URY URY
10 FSM FSM FSM FSM FSM FSM FSM
adm0_a3_ma adm0_a3_pt adm0_a3_ar adm0_a3_jp adm0_a3_ko adm0_a3_vn adm0_a3_tr
1 ZWE ZWE ZWE ZWE ZWE ZWE ZWE
2 ZMB ZMB ZMB ZMB ZMB ZMB ZMB
3 YEM YEM YEM YEM YEM YEM YEM
4 VNM VNM VNM VNM VNM VNM VNM
5 VEN VEN VEN VEN VEN VEN VEN
6 VAT VAT VAT VAT VAT VAT VAT
7 VUT VUT VUT VUT VUT VUT VUT
8 UZB UZB UZB UZB UZB UZB UZB
9 URY URY URY URY URY URY URY
10 FSM FSM FSM FSM FSM FSM FSM
adm0_a3_id adm0_a3_pl adm0_a3_gr adm0_a3_it adm0_a3_nl adm0_a3_se adm0_a3_bd
1 ZWE ZWE ZWE ZWE ZWE ZWE ZWE
2 ZMB ZMB ZMB ZMB ZMB ZMB ZMB
3 YEM YEM YEM YEM YEM YEM YEM
4 VNM VNM VNM VNM VNM VNM VNM
5 VEN VEN VEN VEN VEN VEN VEN
6 VAT VAT VAT VAT VAT VAT VAT
7 VUT VUT VUT VUT VUT VUT VUT
8 UZB UZB UZB UZB UZB UZB UZB
9 URY URY URY URY URY URY URY
10 FSM FSM FSM FSM FSM FSM FSM
adm0_a3_ua adm0_a3_un adm0_a3_wb continent region_un subregion
1 ZWE -99 -99 Africa Africa Eastern Africa
2 ZMB -99 -99 Africa Africa Eastern Africa
3 YEM -99 -99 Asia Asia Western Asia
4 VNM -99 -99 Asia Asia South-Eastern Asia
5 VEN -99 -99 South America Americas South America
6 VAT -99 -99 Europe Europe Southern Europe
7 VUT -99 -99 Oceania Oceania Melanesia
8 UZB -99 -99 Asia Asia Central Asia
9 URY -99 -99 South America Americas South America
10 FSM -99 -99 Oceania Oceania Micronesia
region_wb name_len long_len abbrev_len tiny homepart
1 Sub-Saharan Africa 8 8 5 -99 1
2 Sub-Saharan Africa 6 6 6 -99 1
3 Middle East & North Africa 5 5 4 -99 1
4 East Asia & Pacific 7 7 5 2 1
5 Latin America & Caribbean 9 9 4 -99 1
6 Europe & Central Asia 7 7 4 4 1
7 East Asia & Pacific 7 7 4 2 1
8 Europe & Central Asia 10 10 4 5 1
9 Latin America & Caribbean 7 7 4 -99 1
10 East Asia & Pacific 10 30 6 -99 1
min_zoom min_label max_label label_x label_y ne_id wikidataid
1 0 2.5 8.0 29.92544 -18.911640 1159321441 Q954
2 0 3.0 8.0 26.39530 -14.660804 1159321439 Q953
3 0 3.0 8.0 45.87438 15.328226 1159321425 Q805
4 0 2.0 7.0 105.38729 21.715416 1159321417 Q881
5 0 2.5 7.5 -64.59938 7.182476 1159321411 Q717
6 0 5.0 10.0 12.45342 41.903323 1159321407 Q237
7 0 4.0 9.0 166.90876 -15.371530 1159321421 Q686
8 0 3.0 8.0 64.00543 41.693603 1159321405 Q265
9 0 3.0 8.0 -55.96694 -32.961127 1159321353 Q77
10 0 5.0 10.0 158.23402 6.887553 1159320691 Q702
name_ar name_bn
1 زيمبابوي জিম্বাবুয়ে
2 زامبيا জাম্বিয়া
3 اليمن ইয়েমেন
4 فيتنام ভিয়েতনাম
5 فنزويلا ভেনেজুয়েলা
6 الفاتيكان ভ্যাটিকান সিটি
7 فانواتو ভানুয়াতু
8 أوزبكستان উজবেকিস্তান
9 الأوروغواي উরুগুয়ে
10 ولايات ميكرونيسيا المتحدة মাইক্রোনেশিয়া যুক্তরাজ্য
name_de name_en
1 Simbabwe Zimbabwe
2 Sambia Zambia
3 Jemen Yemen
4 Vietnam Vietnam
5 Venezuela Venezuela
6 Vatikanstadt Vatican City
7 Vanuatu Vanuatu
8 Usbekistan Uzbekistan
9 Uruguay Uruguay
10 Föderierte Staaten von Mikronesien Federated States of Micronesia
name_es name_fa name_fr
1 Zimbabue زیمبابوه Zimbabwe
2 Zambia زامبیا Zambie
3 Yemen یمن Yémen
4 Vietnam ویتنام Viêt Nam
5 Venezuela ونزوئلا Venezuela
6 Ciudad del Vaticano واتیکان Cité du Vatican
7 Vanuatu وانواتو Vanuatu
8 Uzbekistán ازبکستان Ouzbékistan
9 Uruguay اروگوئه Uruguay
10 Estados Federados de Micronesia میکرونزی États fédérés de Micronésie
name_el name_he name_hi
1 Ζιμπάμπουε זימבבואה ज़िम्बाब्वे
2 Ζάμπια זמביה ज़ाम्बिया
3 Υεμένη תימן यमन
4 Βιετνάμ וייטנאם वियतनाम
5 Βενεζουέλα ונצואלה वेनेज़ुएला
6 Βατικανό קריית הוותיקן वैटिकन नगर
7 Βανουάτου ונואטו वानूआटू
8 Ουζμπεκιστάν אוזבקיסטן उज़्बेकिस्तान
9 Ουρουγουάη אורוגוואי उरुग्वे
10 Ομόσπονδες Πολιτείες της Μικρονησίας מיקרונזיה माइक्रोनेशिया के संघीकृत राज्य
name_hu name_id name_it
1 Zimbabwe Zimbabwe Zimbabwe
2 Zambia Zambia Zambia
3 Jemen Yaman Yemen
4 Vietnám Vietnam Vietnam
5 Venezuela Venezuela Venezuela
6 Vatikán Vatikan Città del Vaticano
7 Vanuatu Vanuatu Vanuatu
8 Üzbegisztán Uzbekistan Uzbekistan
9 Uruguay Uruguay Uruguay
10 Mikronéziai Szövetségi Államok Mikronesia Stati Federati di Micronesia
name_ja name_ko name_nl name_pl name_pt
1 ジンバブエ 짐바브웨 Zimbabwe Zimbabwe Zimbábue
2 ザンビア 잠비아 Zambia Zambia Zâmbia
3 イエメン 예멘 Jemen Jemen Iémen
4 ベトナム 베트남 Vietnam Wietnam Vietname
5 ベネズエラ 베네수엘라 Venezuela Wenezuela Venezuela
6 バチカン 바티칸 시국 Vaticaanstad Watykan Vaticano
7 バヌアツ 바누아투 Vanuatu Vanuatu Vanuatu
8 ウズベキスタン 우즈베키스탄 Oezbekistan Uzbekistan Uzbequistão
9 ウルグアイ 우루과이 Uruguay Urugwaj Uruguai
10 ミクロネシア連邦 미크로네시아 연방 Micronesia Mikronezja Micronésia
name_ru name_sv name_tr name_uk name_ur
1 Зимбабве Zimbabwe Zimbabve Зімбабве زمبابوے
2 Замбия Zambia Zambiya Замбія زیمبیا
3 Йемен Jemen Yemen Ємен یمن
4 Вьетнам Vietnam Vietnam В'єтнам ویتنام
5 Венесуэла Venezuela Venezuela Венесуела وینیزویلا
6 Ватикан Vatikanstaten Vatikan Ватикан ویٹیکن سٹی
7 Вануату Vanuatu Vanuatu Вануату وانواتو
8 Узбекистан Uzbekistan Özbekistan Узбекистан ازبکستان
9 Уругвай Uruguay Uruguay Уругвай یوراگوئے
10 Микронезия Mikronesiska federationen Mikronezya Мікронезія مائیکرونیشیا
name_vi name_zh name_zht fclass_iso tlc_diff
1 Zimbabwe 津巴布韦 辛巴威 Admin-0 country <NA>
2 Zambia 赞比亚 尚比亞 Admin-0 country <NA>
3 Yemen 也门 葉門 Admin-0 country <NA>
4 Việt Nam 越南 越南 Admin-0 country <NA>
5 Venezuela 委内瑞拉 委內瑞拉 Admin-0 country <NA>
6 Thành Vatican 梵蒂冈 梵蒂岡 Admin-0 country <NA>
7 Vanuatu 瓦努阿图 萬那杜 Admin-0 country <NA>
8 Uzbekistan 乌兹别克斯坦 烏茲別克 Admin-0 country <NA>
9 Uruguay 乌拉圭 烏拉圭 Admin-0 country <NA>
10 Micronesia 密克罗尼西亚联邦 密克羅尼西亞聯邦 Admin-0 country <NA>
fclass_tlc fclass_us fclass_fr fclass_ru fclass_es fclass_cn fclass_tw
1 Admin-0 country <NA> <NA> <NA> <NA> <NA> <NA>
2 Admin-0 country <NA> <NA> <NA> <NA> <NA> <NA>
3 Admin-0 country <NA> <NA> <NA> <NA> <NA> <NA>
4 Admin-0 country <NA> <NA> <NA> <NA> <NA> <NA>
5 Admin-0 country <NA> <NA> <NA> <NA> <NA> <NA>
6 Admin-0 country <NA> <NA> <NA> <NA> <NA> <NA>
7 Admin-0 country <NA> <NA> <NA> <NA> <NA> <NA>
8 Admin-0 country <NA> <NA> <NA> <NA> <NA> <NA>
9 Admin-0 country <NA> <NA> <NA> <NA> <NA> <NA>
10 Admin-0 country <NA> <NA> <NA> <NA> <NA> <NA>
fclass_in fclass_np fclass_pk fclass_de fclass_gb fclass_br fclass_il
1 <NA> <NA> <NA> <NA> <NA> <NA> <NA>
2 <NA> <NA> <NA> <NA> <NA> <NA> <NA>
3 <NA> <NA> <NA> <NA> <NA> <NA> <NA>
4 <NA> <NA> <NA> <NA> <NA> <NA> <NA>
5 <NA> <NA> <NA> <NA> <NA> <NA> <NA>
6 <NA> <NA> <NA> <NA> <NA> <NA> <NA>
7 <NA> <NA> <NA> <NA> <NA> <NA> <NA>
8 <NA> <NA> <NA> <NA> <NA> <NA> <NA>
9 <NA> <NA> <NA> <NA> <NA> <NA> <NA>
10 <NA> <NA> <NA> <NA> <NA> <NA> <NA>
fclass_ps fclass_sa fclass_eg fclass_ma fclass_pt fclass_ar fclass_jp
1 <NA> <NA> <NA> <NA> <NA> <NA> <NA>
2 <NA> <NA> <NA> <NA> <NA> <NA> <NA>
3 <NA> <NA> <NA> <NA> <NA> <NA> <NA>
4 <NA> <NA> <NA> <NA> <NA> <NA> <NA>
5 <NA> <NA> <NA> <NA> <NA> <NA> <NA>
6 <NA> <NA> <NA> <NA> <NA> <NA> <NA>
7 <NA> <NA> <NA> <NA> <NA> <NA> <NA>
8 <NA> <NA> <NA> <NA> <NA> <NA> <NA>
9 <NA> <NA> <NA> <NA> <NA> <NA> <NA>
10 <NA> <NA> <NA> <NA> <NA> <NA> <NA>
fclass_ko fclass_vn fclass_tr fclass_id fclass_pl fclass_gr fclass_it
1 <NA> <NA> <NA> <NA> <NA> <NA> <NA>
2 <NA> <NA> <NA> <NA> <NA> <NA> <NA>
3 <NA> <NA> <NA> <NA> <NA> <NA> <NA>
4 <NA> <NA> <NA> <NA> <NA> <NA> <NA>
5 <NA> <NA> <NA> <NA> <NA> <NA> <NA>
6 <NA> <NA> <NA> <NA> <NA> <NA> <NA>
7 <NA> <NA> <NA> <NA> <NA> <NA> <NA>
8 <NA> <NA> <NA> <NA> <NA> <NA> <NA>
9 <NA> <NA> <NA> <NA> <NA> <NA> <NA>
10 <NA> <NA> <NA> <NA> <NA> <NA> <NA>
fclass_nl fclass_se fclass_bd fclass_ua geometry
1 <NA> <NA> <NA> <NA> MULTIPOLYGON (((31.28789 -2...
2 <NA> <NA> <NA> <NA> MULTIPOLYGON (((30.39609 -1...
3 <NA> <NA> <NA> <NA> MULTIPOLYGON (((53.08564 16...
4 <NA> <NA> <NA> <NA> MULTIPOLYGON (((104.064 10....
5 <NA> <NA> <NA> <NA> MULTIPOLYGON (((-60.82119 9...
6 <NA> <NA> <NA> <NA> MULTIPOLYGON (((12.43916 41...
7 <NA> <NA> <NA> <NA> MULTIPOLYGON (((166.7458 -1...
8 <NA> <NA> <NA> <NA> MULTIPOLYGON (((70.94678 42...
9 <NA> <NA> <NA> <NA> MULTIPOLYGON (((-53.37061 -...
10 <NA> <NA> <NA> <NA> MULTIPOLYGON (((162.9832 5....
# MANIPULAÇÕES NO BANCO -----------
# OMS
who_trat <- who_bruto %>%
dplyr::select(WHO_region,Date_reported,Country,New_cases,Cumulative_cases, New_deaths)%>%
dplyr::group_by(WHO_region, Country) %>% # agrupando (tabela dinamica)
dplyr::summarise(Acumulado=max(Cumulative_cases, na.rm=T))
# Shape
## Renomeando a coluna para o join.
## Renomeei usando o Rbase porque o dplyr não aceitou mais
names(mundo)[10] <- "Country"
mundo_trat <- mundo Vamos dar olhada num papa genérico.
Mundo:
covid_mundo_com_shape_iso2 <- covid_mundo_com_shape_iso %>%
mutate(Acumulado_cat = cut(Acumulado,
breaks=c(-1,17000,160000,1000000,
max(covid_mundo_com_shape_iso$Acumulado, na.rm=T)+1),
labels=c("até 17 mil", "17 mil - 160 mil ",
"160 mil - 1 milhão", "acima de 1 milhão")))
paleta<-c('#feebe2','#fbb4b9','#f768a1','#ae017e')
ggplot(covid_mundo_com_shape_iso2, fill="white")+
geom_sf(aes(geometry=geometry, fill=Acumulado_cat ),
color="purple", # cor da fronteira
lwd=0.1) + # finura da fronteira
scale_fill_manual(values=paleta, name="Casos Acumulados \n de covid-19 no Mundo")+
theme_map()+
theme(panel.background = element_rect(fill = "lightblue"),
legend.position = "bottom")+
ggtitle("Um mapa bonito")#oceano
ggsave("mapa_rosa.png")